Method to enhance system analysis

ABSTRACT

A wireless network comprises access points (APs) implementing roaming by way of handover of wireless devices between APs. Handover events over an analysis time interval are extracted from a wireless network log of the wireless network. Each handover event comprises a source AP from which a wireless device disconnects and a destination AP to which the wireless device handover events. The graph has nodes representing at least a subset of the APs of the extracted handover events and edges connecting pairs of nodes with each edge having a weight representing a count or frequency of handover events between the APs represented by the pair of nodes connected by the edge. A network diagnostic or visualization task is performed using the HF network topology graph.

FIELD

The following relates generally to the wireless networking arts,wireless networking diagnostic arts, wireless network analysis arts,medical data communications arts, medical information technology (IT)infrastructure arts, and related arts.

BACKGROUND

In hospitals and other healthcare environments, patient monitors areused to monitor the health of individual patients by continuouslymeasuring various parameters such as vital signs (ECG, blood pressure,SPO₂, et cetera), medical therapy regimens (flow rate in intravenousfluid therapy et cetera), and so forth. Patient monitors are usedespecially in intensive care departments, operating rooms, recoveryrooms, ambulances and other environments where the patient's health isat elevated risk.

Patient monitors, therapy devices, and/or other medical devices areoften connected to electronic information centers via a wired and/orwireless communication network. Wireless connectivity is of particularvalue in areas with a high density of medical devices (intensive caredepartments, emergency rooms, et cetera) as wired medical devices canintroduce trip hazards and limit mobility of patients and medicalpersonnel. For wireless connectivity, the wireless network may be adedicated clinical wireless communication network or a general purposewireless network. The wireless network technology is often Wi-Fi, andthe Wi-Fi network servicing medical devices may be a dedicated networkfor medical devices or may also service other communication needs in thehospital. For many routine clerical or other business tasks, Wi-Fiquality issues such as long latency times may be tolerable; however, formedical devices, uninterrupted, low-delay transport of patient data tothe information centers is desired to facilitate surveillance ofmultiple patients by a single nurse by using an information center. Theinformation center typically has one or more screens to display the dataof multiple patients simultaneously.

To ensure reliability of the wireless network, especially for medicaldevice communications (though not limited thereto), provision is made tomonitor and troubleshoot possible problems, such as device failures orunacceptable delays in the communication network. For this purpose, anapplication monitor may be employed. An example of such an applicationmonitor is the Philips PerformanceBridge® Focal Point on-premisemanagement system available from Koninklijke Philips N.V. (formerlyknown as Philips FocusPoint). This application monitor executes on oneor more server computers (possibly employing a cloud implementation) andis connected with the wireless network to continuously track theaccessibility and performance of individual wireless and wired medicaldevices. Remote monitoring engineers (RMEs) and Remote Service Engineers(RSEs) in call centers can make use of Focal Point to investigateproblems in the network by using the data gathered from the network.

In a large hospital or other sizable medical institution, the patientmonitoring network (or networks) can be large and complex, presenting asubstantial challenge to localize a problem from its symptoms in thedata. The problem is enhanced in the case of a wireless networksupporting mobile devices (e.g., medical devices on trolleys or securedto patient beds, which may move with an ambulatory patient or with apatient being transported on a gurney) since a moving wireless medicaldevice is frequently handed over from one wireless access point (AP) toanother. A typical troubleshooting approach employs a divide and conquerstrategy in which the network is recursively split in parts and eachsub-part is analyzed to determine if the issue is in that part. In thisway, the fault can be progressively localized to identify a specific AP,router, hub, switch, or other malfunctioning network component.

Efficient implementation of a divide-and-conquer diagnostic strategy isassisted by an accurate understanding of the physical topology of thewireless network, especially the locations in two- or three-dimensionalspace of the APs. However, knowledge of the physical topology is oftenunavailable, or inaccurate and/or out-of-date. In a sizable medicalinstitution, APs may frequently be added, removed, physically relocated,replaced with different/newer models, or so forth on an ad hoc basis.

The following discloses certain improvements.

SUMMARY

In some non-limiting illustrative embodiments disclosed herein, anetwork diagnostic system is disclosed for analyzing a wireless networkcomprising a plurality of access points (APs) implementing roaming byway of handover of wireless devices between APs. The network diagnosticsystem comprises an electronic processor and a non-transitory storagemedium storing instructions readable and executable by the electronicprocessor to perform a network diagnostic method. The network diagnosticmethod includes: extracting a global handover frequency (HF) graph fromcounts of handover events over an analysis time interval in the wirelessnetwork wherein each handover event comprises a source AP from which awireless device disconnects and a destination AP to which the wirelessdevice connects; constructing a HF network topology graph for theanalysis time interval from the extracted global HF graph wherein the HFnetwork topology graph has nodes representing at least a subset of theAPs of the extracted handover events and edges connecting pairs of nodeswith each edge having a weight representing a count or frequency ofhandover events between the APs represented by the pair of nodesconnected by the edge; and performing a network diagnostic orvisualization task using the HF network topology graph.

In some non-limiting illustrative embodiments disclosed herein, awireless network comprises a plurality of APs configured to implementroaming by way of handover of wireless devices between APs, and anetwork diagnostic system as set forth in the immediately precedingparagraph. In some embodiments, the plurality of APs are configured toimplement a Wi-Fi network.

In some non-limiting illustrative embodiments disclosed herein, adiagnostic method is performed in conjunction with a Wi-Fi networkcomprising a plurality of APs. The diagnostic method comprises:constructing or retrieving from memory a HF network topology graphhaving nodes representing APs and edges connecting pairs of nodeswherein the edges have weights representing counts or frequencies ofhandover events between the APs represented by the connected pair ofnodes; creating a rendering of the HF network topology graph; anddisplaying the rendering on a display.

In some non-limiting illustrative embodiments disclosed herein, anon-transitory storage medium stores instructions readable andexecutable by an electronic processor to perform a diagnostic method foranalyzing a wireless network comprising a plurality of APs implementingroaming by way of handover of wireless devices between APs. Thediagnostic method comprises: constructing a HF network topology graphfrom handover events in the wireless network wherein the HF networktopology graph has nodes representing APs and edges connecting pairs ofnodes with weights representing counts or frequencies of handover eventsbetween the APs represented by the connected pairs of nodes; andperforming a network diagnostic or visualization task using the HFnetwork topology graph.

One advantage resides in providing a wireless network with improvednetwork topology information that is more informative than a physicalnetwork topology based solely on the physical locations of wirelessaccess points.

Another advantage resides in providing a wireless network with networktopology information automatically derived from roaming behavior ofwireless devices on the network.

Another advantage resides in providing a wireless network with networktopology information that is derived without tedious human effort.

Another advantage resides in providing a wireless network with networktopology information that reflects actual performance of wireless accesspoints of the wireless network.

Another advantage resides in providing a wireless network with networktopology information that can be generated for user-chosen timeintervals.

A given embodiment may provide none, one, two, more, or all of theforegoing advantages, and/or may provide other advantages as will becomeapparent to one of ordinary skill in the art upon reading andunderstanding the present disclosure.

BRIEF DESCRIPTION OF THE DRAWINGS

The invention may take form in various components and arrangements ofcomponents, and in various steps and arrangements of steps. The drawingsare only for purposes of illustrating the preferred embodiments and arenot to be construed as limiting the invention.

FIG. 1 diagrammatically illustrates a Wireless Devices Management System(WDMS) having network topology discovery capability and providingnetwork diagnostics employing the discovered network topology.

FIG. 2 diagrammatically shows an illustrative method of network topologydiscovery suitably performed by the WDMS of FIG. 1.

FIG. 3 diagrammatically shows an illustrative method of network topologyrendering suitably performed by the WDMS of FIG. 1.

FIG. 4 diagrammatically shows an illustrative embodiment of therendering output by the method of FIG. 3.

DETAILED DESCRIPTION

As noted previously, efficient implementation of a divide-and-conquerdiagnostic strategy is assisted by an accurate understanding of thephysical topology of the wireless network, especially the locations intwo- or three-dimensional space of the APs. However, knowledge of thephysical topology is often unavailable, or inaccurate and/orout-of-date. In a sizable medical institution, APs may frequently beadded, removed, physically relocated, replaced with different/newermodels, or so forth on an ad hoc basis. Attempting to construct andmaintain an up-to-date map of the physical locations of the APsdistributed throughout a medical institution would be a time consumingand laborious task which is prone to error.

Moreover, it is recognized herein that the physical network topology isnot optimal for performing network diagnosis. This is because theeffective range of the wireless connectivity of an AP can vary due tonumerous (and often interrelated) factors impacting the radio frequency(RF) signal, such as physical RF barriers, RF interference from otherAPs or other RF interference sources, the operating RF channel, RF radiocharacteristics, and so forth. The effective range of the wirelessconnectivity of an AP can vary over time (e.g., impacted by when someother RF interference source is operating and its transmission strength)and can be directionally dependent. Hence, even if an up-to-date map ofthe physical locations of the APs is available, such a map would not beideal for diagnostic purposes such as identifying partitions of thenetwork for divide-and-conquer diagnosis.

In embodiments disclosed herein, network topology discovery andrendering approaches are disclosed. These approaches leverage dataacquired about handover events in which a wireless medical device ishanded over from one AP to another AP. Such handover events are themechanism by which a wireless device can roam about the medicalinstitution—as the device moves further away from the AP to which it iscurrently (wirelessly) connected, the RF signal weakens; concurrently,the signal strength to some other AP may be increasing, e.g. if thedevice is moving toward that other AP. The Wi-Fi network providesstandard protocols by which a handover occurs in which the moving devicedisconnects from its current AP and connects to the new AP with strongerRF signal strength (i.e., the device is handed off from the one AP tothe next AP). Such handover events are routinely recorded by a WirelessDevices Management System (WDMS) and/or by backbone systems of the Wi-Finetwork itself. The disclosed network topology discovery techniques arepremised on the observation herein that if two APs have frequenthandovers of devices between them, then the two APs are physically closetogether and, more importantly, are close together in the sense ofproviding significantly overlapping wireless coverage areas. To providean example, consider two APs designated AP(i) and AP(j). If there aremany handover events of devices from AP(i) to AP(j) and vice versa, thenit follows that AP(i) and AP(j) have significantly overlapping wirelesscoverage areas. Conversely, if there are never any handover events fromAP(i) to some other AP(k) or vice versa, then it follows that AP(i) andAP(k) have coverage areas that do not have any significant overlap.

Accordingly, as disclosed herein, a network topology is discovered byanalysis of the log of handover events maintained by the WDMS and/or thenetwork backbone system. This network topology can be discovered in acompletely automated fashion by leveraging existing handover events logdata, thus eliminating the laborious construction of a physical map ofAP locations. Moreover, the discovered network topology is more usefulfor network analysis than would be a physical map of AP locations. Thisis because the discovered network topology automatically and empiricallyaccounts for directional dependency of the effective ranges of thewireless connectivity of the respective APs, as these factors naturallyimpact the handover frequencies between pairs of APs. In a variantaspect, the time interval of the handover events log used to generatethe network topology can be varied, with different network topologiesdiscovered from different log time intervals, in order to investigatetime variations in the network topology so as to potentially isolate anintermittent network problem.

The network topology discovered from analysis of handover events logdata as disclosed herein is referred to herein as a handover frequencynetwork topology, or “HF network topology” in order to distinguish itfrom a physical network topology that represents the physical locationsof the APs. As just explained, the HF network topology advantageouslycaptures directional (and optionally also temporal) dependence of thewireless coverage ranges of the APs in a way that a physical networktopology cannot, thus making the HF network topology disclosed hereinespecially useful for network diagnostic tasks such asdivide-and-conquer fault localization.

With reference to FIG. 1, a wireless network includes a plurality ofwireless access points (APs) 10, which are distributed in area(s) of ahospital or other medical institution where access to the wirelessnetwork is intended to be provided. This may be the entire hospital, oronly selected portions. Each of the APs 10 includes a wirelesstransceiver and is configured to communicate with wireless devices 12,14, 16 on the wireless network. In addition to the APs 10, the wirelessnetwork may optionally include other conventional components not shownin FIG. 1 such as routers, hubs, network switches, and/or so forth toprovide wired and/or wireless connection of the APs 10 to a networkbackbone implemented on one or more server computers (optionallycloud-based), to other wired and/or wireless networks and/or theInternet, or so forth as is known in the art. In some embodiments, thewireless network is a Wi-Fi network in which the APs 10 communicate withthe wireless devices 12, 14, 16 using an IEEE 802.11-compliant wirelesscommunication protocol. However, this is merely an illustrative exampleand it is contemplated for the wireless network to be configured inaccordance with another wireless standard such as WiMax employing anIEEE 802.16-compliant wireless communication protocol or WirelessMedical Telemetry Service (WMTS). The illustrative wireless devices arewireless medical devices, such as illustrative patient monitors 12, 14and an illustrative ultrasound scanner 16. These are merely illustrativeexamples of wireless devices, and more generally the wireless devicesmay be any type of wireless medical device (e.g. ECG monitors, SPO₂monitors, or so forth, medical therapy devices such as an intravenous(IV) infusion pump, a hub node of a medical body area network (MBAN),and/or so forth. Even more generally, while the disclosed networktopology discovery approaches are described in the context of a wirelessnetwork for medical devices, it is more broadly contemplated for thewireless devices communicating with the APs 10 of the wireless networkto be wireless devices which are not medical devices, e.g. tabletcomputers, notebook computers, cellular telephones (“cellphones”),streaming media players with Wi-Fi connectivity, and/or so forth.

The illustrative wireless network further includes a Wireless DevicesManagement System (WDMS) 20 which comprises an application program (orsuite of application programs) running on an electronic processor 22(e.g. a server computer, a cluster of server computers, a cloudcomputing resource, various combination thereof, or so forth) forproviding functionality such as generating network statistics and keyperformance indicators (KPIs), generating network fault alerts,performing network capacity analyses, hardware and software auditing,and/or so forth. In some embodiments, the WDMS 20 is implemented as aninstallation of the Philips PerformanceBridge® Focal Point on-premisemanagement system available from Koninklijke Philips N.V., although thisis merely an illustrative example. In some embodiments, the WDMS 20 is ageneral-purpose wireless network management system that is not specificto the medical domain. Among its various functions, the WDMS 20maintains a wireless network log 24 which is stored on a non-transitorystorage medium. While a single network log 24 is illustrated andreferred to therein, it is contemplated that the network log 24 may belogically implemented as multiple logs storing different types of dataor storing the same data with different sortings/formats/et cetera;and/or the network log 24 may be physically stored in multiple segmentson different storage media. It will be further appreciated that the WDMS20 is implemented as instructions stored on a non-transitory storagemedium (which may be the same as or different from the non-transitorystorage medium on which the network log 24 is stored), the storedinstructions being readable and executable by the electronic processor22 to implement the WDMS 20. The non-transitory storage medium or mediamay, by way of non-limiting illustrative example, comprise a hard diskor other magnetic storage medium, a solid state drive (SSD), flashmemory, or other electronic storage medium, an optical disk or otheroptical storage medium, various combinations thereof, and/or so forth.The WDMS 20 also generates a diagnostic user interface (UI) 26 withwhich information technology (IT) personnel or other user(s) interactvia a desktop, notebook, or other computer or dumb terminal 30 having adisplay 32 and one or more user input devices 34 (e.g. an illustrativekeyboard and trackpad, and/or a mouse, touch-sensitive overlay of thedisplay 32, various combinations thereof, and/or so forth). For example,the user may interact with the diagnostic UI 26 via the computer orterminal 30 to retrieve and display graphs or other representations ofKPIs, network statistics, usage forecasts, or other informationcollected and/or generated by the WDMS 20. Much of this information maybe generated from content of the network log 24.

With continuing reference to FIG. 1, as disclosed herein the WDMS 20 isfurther capable of performing network topology discovery by analyzinghandover events extracted from the wireless network log 24. Said anotherway, the instructions stored on the non-transitory storage medium (ormedia) are readable and executable by the electronic processor 22 toperform network topology discovery by analyzing handover eventsextracted from the wireless network log 24. This functionality isdiagrammatically indicated in FIG. 1 by a network topology discoveryblock 40 that extracts handover events over an analysis time intervalfrom the wireless network log 24 of the wireless network and constructsat least one handover frequency (HF) network topology graph 44 for theanalysis time interval from the extracted handover events. The HFnetwork topology graph(s) 44 may be stored in memory (that is, may bestored on a non-transitory storage medium) and later retrieved from thememory. A rendering engine 46 optionally renders a graphicalrepresentation of the HF network topology graph 44. The graphicalrepresentation may be displayed on the display 32 which is operativelyconnected with the electronic processor 22.

With continuing reference to FIG. 1 and with further reference to FIG.2, a method suitably performed by the network topology discovery block40 is described. The first step is to extract handover events from thewireless network log 24. Each handover event comprises (or is suitablydefined in terms of) a source AP from which a wireless device (e.g. oneof the devices 12, 14, 16 of FIG. 1) disconnects and a destination AP towhich the wireless device connects. The detailed operation of thehandover events extraction depends on the nature of the logged data. Forexample, if both the AP disconnect and AP connect actions are logged asa grouped pair, then the handover events extraction merely retrievesthose grouped pairs.

In the illustrative example of FIG. 2, however, the logged data is atimestamped connection action, that is, the action in which a wirelessdevice connects with an AP. The AP disconnect action is not logged inthis example. Hence, in this example, for each wireless device, itsroaming behaviour data consists of a list of timestamped connect events,i.e., pairs (t, v), indicating the time t at which the wireless deviceestablished a connection with the AP designated as v. This list, orderedin time, is referred to herein as the trajectory of the wireless device.Extracted data 50 shown in FIG. 2 thus includes a set of trajectoriesfor devices designated “Dev1”, “Dev2”, “Dev3”, . . . . The trajectoriesare collected for some analysis time interval, for example all datacollected in the last month. Optionally, the analysis time window may bea sliding window, e.g. a sliding one-month window with data over onemonth old being discarded on a sliding basis. It is contemplated for theanalysis time window to be the entire operating life to date of thewireless network, so that the extracted trajectory for each wirelessdevice includes all APs it has ever been connected to since it wasinstalled in the system. The time-wise successive elements of thetrajectory denote successive connections, and handover events are thendefined by adjacent pairs of (t, v) values. By way of illustrativeexample, the partial sequence . . . (t_(i−1), v_(i−1)), (t_(i), v_(i)),(t_(i+1), v_(i+1)) . . . defines: a first handover event in which the APdesignated as v_(i−1) is the source AP from which the device disconnectsand the AP designated as v_(i) is the destination AP to which the deviceconnects; and a second handover event in which the AP designated asv_(i) is now the source AP and the AP designated as v_(i+1) is thedestination AP.

The handover events extraction process as described thus far assumesthat the wireless device remains connected to the wireless network. Thisis not always the case. For example, a wireless patient monitor may beconnected to an AP designated v_(x), then taken off the network for someperiod of time (for example, the patient is discharged and the patientmonitor is temporarily disconnected from the network), and then laterconnected to some other AP designated v_(y) (for example, when it is nowconnected to a newly admitted patient). The resulting sequence ofconnection events . . . (t_(x), v_(x)), (t_(y), v_(y)) . . . does notrepresent a handover event from v_(x) to v_(y)—indeed, those two APs maybe in completely different parts of the hospital and so widely separatedthat no handover could ever occur between them. To handle thissituation, a placeholder “AP” can be designated, e.g. named EMPTY forease of reference, and also treated as an AP and is used to indicatethat the wireless device is not connected to any AP. The sequence ofconnection events thus becomes . . . (t_(x), v_(x)), (t_, EMPTY),(t_(y), v_(y)) . . . . The pair (t_, EMPTY) indicates that the monitorwas disconnected from the last AP (v_(x)) without being connected to anynew AP. Hence, a handover event from v_(x) to v_(y) is (correctly) notidentified. With this modification, a trajectory for a wireless devicecomprises a time-ordered sequence of timestamped events (t, v) (where tis referred to as the timestamp here and v is the event, i.e. the actionof connecting to the AP designated as v) in which each timestamped eventof the trajectory is one of (i) a connection event in which the wirelessdevice connects with an AP (designated as v in the illustrativenotation) or (ii) a disconnection event in which the wireless devicedisconnects from all APs of the wireless network (indicated by the“connection” event (t_, EMPTY) in the illustrative notation).

The number of extracted handover events between each pair of APs, e.g.between AP_(x)↔AP_(y) (where the double-headed arrow indicates countingboth handovers from AP_(x) to AP_(y) and also handovers from AP_(y) toAP_(x)) is then counted to create a global handover frequency (HF) graph52. It will be appreciated that in FIG. 2 the global HF graph 52 isrepresented in a table or list form, but that this can be graphicallyvisualized by plotting each AP as a node and adding edges between thenodes having respective weights corresponding to the counts, e.g.AP_(R)↔AP_(y) can be represented by a node AP_(x), a node AP_(y), and anedge between these nodes labelled with the count of handover eventsAP_(x)↔AP_(y).

In another approach, the extraction of the handover events and theconstruction of the graph can be combined as follows. The global HFgraph 52 may be represented as a graph

_(global)=(V,E) where the set V of vertices consists of all the APs thatoccur in the trajectories. The set E of edges is constructed as follows.The trajectories of each wireless monitor m is used as follows. Let m'strajectory have length l and be given by [(t₁, v₁), (t₂, v₂), . . . ,(t₁, v₁)]. It is assumed that t_(i)<t_(i+1) and v_(i)≠v_(i+1) for i=1,2, . . . , l−1. For each i=1, 2, . . . , l−1, if there is not yet anedge e=(v_(i), v_(i+1)) in E, it is created, added to E, and obtains aweight of 1. If it already exists in E, its weight is increased by 1.

The global HF graph 52 is not, in general, a connected graph. Usually,the global HF graph 52 consists of k≥1 mutually disconnected subgraphs.If k is chosen as large as possible, then each of the subgraphs isitself a connected graph. The global HF graph 52 may be disconnected forvarious reasons. The hospital may have areas that are not covered byAPs, so that there is no way for a wireless device to roam from one APto some other AP without temporarily disconnecting from the wirelessnetwork. Even if the entire hospital is covered by the wireless network,there may be regions between which no devices ever roam. For example,wireless devices may never move into, or out of, in a medical isolationward, or devices may never move into, or out of, a maternity ward. (Onthe other hand, even if a wireless device never enters an area such as,e.g., a maternity ward, roaming to an AP in the maternity ward may stillbe possible and, if so, will likely happen occasionally). These factorscan make the global HF graph 52 not ideal for visualization.

Accordingly, a topology extraction process 54 is performed. The topologyextraction process 54 operates to segment the global HF graph 52 intoone or (typically) more handover frequency (HF) network topology graphs44, each of which is a connected graph, and/or extracting the HF networktopology graph(s) 44 as maximum weight spanning trees. Each HF networktopology graph has nodes representing at least a subset of the APs ofthe extracted handover events and edges connecting pairs of nodes witheach edge having a weight representing a count or frequency of handoverevents between the APs represented by the pair of nodes connected by theedge. The terms “count” or “frequency” are functionally synonymous for agiven analysis time interval. For example, if the analysis time intervalis 30 days, one can represent the count of handover events for an edgebetween a given AP pair as the total count of handover events over the30 days, or this count can be normalized to a “per day” value bydividing by 30 which then provides the (average) counts per day, whichis a frequency value. Such normalization is not of significance as therelative values of the edges in the graph does not change.

Optionally, the topology extraction process 54 may remove any edgeswhose count or frequency is below some threshold value. This amounts topruning the HF network topology graph 44 of low count (or low frequency)edges. Additionally or alternatively, a display threshold (e n) may beapplied during the visualization stage. The pruning may optionally alsoremove nodes. For example, single nodes without any (unpruned) edgesattached to them may be removed. In another variant, the pruning mayremove small trees with only a few nodes, even if the edges still haveweight greater than the threshold value.

The resulting HF network topology graph (or graphs) 44 advantageouslyprovide a useful representation of the physical topology of the APs.Unlike such a purely physical topology which represents the actualdistances between APs, the HF network topology graph 44 accounts fordifferences in the wireless connectivity ranges amongst the various APs,and represents functionally relevant information about which AP pairsengage in the most frequent handover events.

Advantageously, the network topology discovery process 40 is empiricalas it operates on the counts of handover events (that is, the global HFgraph 52) extracted from the wireless network log 24. This extraction iscomputationally efficient and uses information readily available in thenetwork log 24. The extraction also does not require any user inputs(although an input such as a user-designated analysis time interval,and/or a pruning threshold, may optionally be provided). The extractionalso does not require a hospital map or physical locations of the APs.

The HF network topology graph 44 can be computed for a user-chosenanalysis time interval, so that for example if a network fault isidentified which has been observed only for the last 24 hours then thenetwork topology discovery process 40 can be applied for an analysistime interval of only the last 24 hours in order to visualize thenetwork topology with the fault. Optionally, the network topologydiscovery process 40 can also be run for an earlier analysis timeinterval prior to when the network fault arose, and comparison of these“before and after” HF network topologies may localize the fault to theregion where these two topologies differ.

While the network topology discovery process 40 does not requireadditional information beyond the extracted handover events, ifadditional information is available it can optionally be used to augment(e.g. annotate) the discovered HF network topology graph (or graphs) 44.For example, FIG. 2 diagrammatically indicates an AP configuration table56 being used in a labeling process 58 to label APs of the HF networktopology graph (or graphs) 44 with information about the APs containedin the AP configuration table 56.

With reference to FIG. 3, an illustrative approach for generating andrendering the network topology graph(s) 44 is described. Thisillustrative topology extraction process 54 employs a maximum-weightspanning tree by making use of, e.g., Kruskal's algorithm (Kruskal, “Onthe shortest spanning subtree of a graph and the Travelling SalesmanProblem”, Proceedings of the American Mathematical Society, Vol. 7, No.1, pp. 48-50, 1956). Since Kruskal's algorithm finds minimum weightspanning trees whereas it is desired here to find maximum weightspanning trees, the topology extraction process 54 includes an operation62 of negating the weights (i.e. edge weight e→−e) for all edges of theglobal HF graph 52. Kruskal's algorithm is then applied in an operation64 to generate one or more minimum weight spanning trees, with theweights inverted in sign per operation 62. Hence, the “minimum weights”are the largest negated weights (i.e. largest negative weights). In anoperation 66, operation 62 is undone for the minimum weight spanningtree(s) (i.e. edge weight e→−e for all edges of the minimum weightspanning tree(s)) thus converting to maximum weight spanning tree(s)that serve as the HF network topology graph(s) 44. These are input tothe diagnostic UI 26 and displayed on the display 32. Optionally, thediagnostic UI 26 includes a display threshold 68 which provides for notdisplaying edges of the maximum weight spanning tree whose weights arebelow some threshold e_(min). Optionally, the user can adjust thedisplay threshold e_(min).

Note that if there is more than one minimum weight spanning tree in theglobal HF graph 52 (with edges inverted per operation 62), thenKruskal's algorithm applied in the operation 64 creates all the minimumweight spanning trees simultaneously. Hence, the application ofKruskal's algorithm in operation 64 advantageously serves toautomatically segment the global HF graph 52 into spanning trees for theconnected subgraphs contained in the global HF graph 52.

With continuing reference to FIG. 1 and with further reference to FIG.4, an illustrative display of a maximum weight spanning tree generatedby the topology extraction process 54 and presented on the display 32 bythe diagnostic UI 26 is shown. In this example, each AP is representedby its MAC address of the form “xx-xx-xx-xx-xx-xx”, and edges withweights above the display threshold 68 are shown. FIG. 4 is for a singleselected connected graph, that is, for one graph of the HF networktopology graph(s) 44. In the illustrative example of FIG. 4, this graphcorresponds to the radiology wing AP network, as indicated in the titleshown on the display 32. (This textual identification of the particulargraph being rendered may, for example, be obtained from the APconfiguration 56 as mentioned referencing FIG. 2).

Although not shown in the example of FIG. 4, it is contemplated for theedges of the rendered maximum weight spanning tree to have line widthscorresponding to the edge weights, so as to graphically emphasize edgesrepresenting AP-AP connections with high handover frequency. A sliderbar 70 provides a user input via which the user can adjust the displaythreshold 68: sliding the marker closer to the upper “Max” end lowersthe display threshold emirs so as to show more connections, whilesliding the marker closer to the lower “Min” end increases the displaythreshold emirs so as to show fewer connections. The analysis timeinterval may optionally also be adjusted using a slider 72. Thisapproach is suitable if the topology discovery process 40 issufficiently fast to enable approximately real-time updating. (If theupdate is too slow, then the analysis time interval may be input priorto the rendering; or in other embodiments the analysis time interval maybe a fixed predetermined value; in either of such alternative cases theslider 72 would be omitted). As explained in a legend 74 in the lowerleft of the display 32, various features in the maximum-weight spanningtree may be denoted by defined symbols: in the illustrative example APswith high load are so denoted, as are AP-AP connections with highhandover rate. Other features could be similarly denoted, such as APsand/or edges with a large change in handover frequency between twographs computed for two different analysis time intervals (e.g., a firsttime interval before some problem was observed, and a second timeinterval after the problem was observed, so that the APs and/or edgeswith large change in handover frequency may be related to the observedproblem).

The illustrative maximum-weight spanning tree rendering is anillustrative visualization approach. Alternative renderings could beemployed for rendering the HF network topology graph 44. Anothercontemplated rendering is a gravitational model in which pairs of nodesare spaced apart by distances computed by balancing a repulsive forceand an attractive force wherein the attractive force for each pair ofnodes is scaled by the weight of the edge connecting the pair of nodes.Another contemplated rendering is a mapping of the nodes of the HFnetwork topology graph 44 to positions on a hexagonal grid such that thetotal weighted distance between connected nodes is (at leastapproximately) minimized. Yet another contemplated rendering is amapping of the nodes to the physical three-dimensional locations of theAPs, with the links having line thicknesses representing the handoverfrequency weights (here it is assumed that a mapping of APs to physicallocations is available). Again, these are merely further non-limitingillustrative rendering examples.

The topology discovery process 40 can be repeated on a, for example,periodic basis or upon request. In this way, the topology informationcan be kept up to date and extensions or changes can be incorporated. Itis also noted that while the rendering 46 is one useful application ofthe discovered HF network topology graph(s) 44, other applications mayconsume the HF network topology graph(s) 44 without graphical renderingand display.

In the following, some wireless network diagnostics that can beperformed using the HF network topology graph(s) 44 are described.

Failures in the system are reported by the WDMS 20 as alerts. Examplealerts are Monitoring Drop-out, Device Rebooted, and Application Error.If an alert becomes active, action may have to be undertaken bytechnical personnel to identify the root cause. For large, complexnetworks, this may be a daunting task, especially if there are multiplealerts becoming active more or less simultaneously, or one alert becomesactive for multiple devices more or less simultaneously. Theavailability of topology information as well as the current locations ofthe affected devices via the HF network topology graph(s) 44 enables anassessment of the locality and size of the problem at hand and allows toapply a ‘divide and conquer’ troubleshooting strategy. For example, if anumber of monitors are involved, then by their current location, it canbe established whether they are physically close to each other.

Network topology optimization applications can also benefit from the HFnetwork topology graph(s) 44. By examining the maximum-weight spanningtrees representing the various wireless networks, it can easily bedetected how intensely a certain wireless network (or portion thereof)is being used in the sense that how much roaming occurs amongneighboring APs. Advantageously, the HF network topology graph(s) 44quantitatively represent the extent of roaming in terms of handoverfrequencies between the various AP-AP pairs. Excessive roaming mayindicate low signal values or non-optimal positioning of APs in terms ofdistance or because of interfering objects. Excessive roaming can leadto data loss (which can be particularly problematic for critical patientdata) and may result in a Monitoring Dropout alert. Applying changes tothe topology can improve the quality of the network and reduce data lossand the number of alerts. Similarly, bottlenecks can be detected byanalyzing the load on APs that are close together via renderings of theHF network topology graph(s) 44. It is thereby possible to identifyregions of high load, where the placement of additional APs may bebeneficial.

These are merely some non-limiting illustrative applications of thedisclosed HF network topology graph(s) 44.

The invention has been described with reference to the preferredembodiments. Modifications and alterations may occur to others uponreading and understanding the preceding detailed description. It isintended that the exemplary embodiment be construed as including allsuch modifications and alterations insofar as they come within the scopeof the appended claims or the equivalents thereof.

1. A network diagnostic system for analyzing a wireless networkcomprising a plurality of access points, APs, implementing roaming byway of handover of wireless devices between APs, the network diagnosticsystem comprising: an electronic processor; and a non-transitory storagemedium storing instructions readable and executable by the electronicprocessor to perform a network diagnostic method including: extracting aglobal handover frequency, HF, graph from counts or frequencies ofhandover events over an analysis time interval in the wireless networkwherein each handover event comprises a source AP from which a wirelessdevice disconnects and a destination AP to which the wireless deviceconnects; constructing a HF network topology graph for the analysis timeinterval from the extracted global HF graph wherein the HF networktopology graph has nodes representing at least a subset of the APs ofthe extracted handover events and edges connecting pairs of nodes witheach edge having a weight representing a count or frequency of handoverevents between the APs represented by the pair of nodes connected by theedge; and performing a network diagnostic or visualization task usingthe HF network topology graph.
 2. The network diagnostic system of claim1 wherein the performing comprises: rendering a graphical representationof the HF network topology graph; and displaying the rendered graphicalrepresentation of the HF network topology graph on a display operativelyconnected with the electronic processor.
 3. The network diagnosticsystem of claim 2 wherein the constructing of the HF network topologygraph comprises: creating one or more maximum weight spanning trees forthe global HF graph; and displaying a selected one of the one or moremaximum weight spanning trees on the display.
 4. The network diagnosticsystem of claim 2 wherein the rendering comprises: rendering thegraphical representation of the HF network topology graph with pairs ofnodes spaced apart by distances computed by balancing a repulsive forceand an attractive force wherein the attractive force for each pair ofnodes is scaled by the weight of the edge connecting the pair of nodes.5. The network diagnostic system of claim 2 wherein the displaying ofthe rendered graphical representation of the HF network topology graphincludes: displaying a user dialog via which a user selects a pruningthreshold; and displaying the rendered graphical representation of theHF network topology graph omitting any edges having weights less thanthe user-selected pruning threshold.
 6. The network diagnostic system ofclaim 1 wherein: the extracting and constructing are repeated for twodifferent analysis time intervals whereby HF network topology graphs forthe two different analysis time intervals are constructed; and theperforming of the network diagnostic or visualization task includescomparing the HF network topology graphs for the two different analysistime intervals.
 7. The network diagnostic system of claim 1 wherein theextracting of the global HF graph comprises: for each of a plurality ofwireless devices connected to the wireless network during the analysistime interval: defining a trajectory for the wireless device comprisinga time-ordered sequence of timestamped events in which each timestampedevent of the trajectory is one of (i) a connection event in which thewireless device connects with an AP or (ii) a disconnection event inwhich the wireless device disconnects from all APs of the wirelessnetwork, and for each adjacent pair of timestamped events of thetrajectory in which both events of the pair are connection events,extracting a handover event.
 8. The network diagnostic system of claim 1wherein the constructing comprises: performing a topology extractionprocess on the global HF graph which segments the global HF graph into aplurality of mutually disconnected HF network topology graphs eachincluding a subset of the APs of the global HF network topology graph.9. The network diagnostic system of claim 1, further comprising aplurality of access points, APs, configured to implement roaming by wayof handover of wireless devices between APs.
 10. (canceled)
 11. Acomputer-implemented diagnostic method performed in conjunction with aWi-Fi network comprising a plurality of access points, APs, thediagnostic method comprising: extracting a global handover frequency,HF, graph from counts or frequencies of handover events over an analysistime interval in the wireless network wherein each handover eventcomprises a source AP from which a wireless device disconnects and adestination AP to which the wireless device connects; constructing ornetwork topology graph for the analysis time interval from the extractedglobal HF graph, the HF network topology graph having nodes representingat least a subset of the APs of the extracted handover events and edgesconnecting pairs of nodes wherein the edges have weights representingcounts or frequencies of handover events between the APs represented bythe connected pair of nodes; performing a network diagnostic orvisualization task using the HF network topology graph. creating arendering of the HF network topology graph; and displaying the renderingon a display.
 12. The diagnostic method of claim 11 wherein theconstructing comprises: creating at least one maximum weight spanningtree for the global HF graph; wherein the displaying comprisesdisplaying a selected one of the at least one maximum weight spanningtree on the display.
 13. The diagnostic method of claim 12 wherein thedisplaying of the selected one of the at least one maximum weightspanning tree includes: displaying a user dialog on the display viawhich a user selects a display threshold; and displaying the selectedone of the at least one maximum weight spanning tree on the displayomitting any edges having weights less than the user-selected displaythreshold.
 14. (canceled)
 15. The diagnostic method of claim 12 whereinthe creating of the at least one maximum weight spanning tree includespruning the at least one maximum weight spanning tree. 16-20. (canceled)